Other papers compared different ML models to identify the most accurate for their application. In [1]}, the authors found that results of the MLP-ANN were very close to that of an SVR. Similar findings were reported in [2]}, [3]}, where the accuracy of the RBF compared to the MLP-ANN was only marginally higher. In [4]}, the authors concluded that for their setup, the WNN implementation was more accurate than the RBF-ANN. In [5]}, the authors found that the ESN was more accurate than an SVR implementation, but also more time consuming during training. In [6]}, the CNN outperformed a standard ANN for predicting PG over irregular terrain. In [7]}, the authors also found that their CNN was more accurate than an MLP-ANN classifier and a stacked auto-encoder (SAE). RNNs have also been found more accurate and faster than MLP-ANNs in the modeling of time-varying communication channels [8]}, [9]}, [10]}. Among the RNN models, the authors in [8]} found that GRU outperformed their LSTM implementation. On the other hand, LSTMs were found more accurate than both standard RNNs [9]}, [13]}, as well as GRUs [9]}.
